Future Sense Blog

The work we are doing to bring low cost sensor tech to smallholder agriculture has the potential to do a number of things. Certainly, we will start with the delivery of useful information to farmers based on more precise data from their fields. We will build a service that supports better decision-making and guides farmers to achieve optimal production outcomes. But there is another value being created through our sensor work and the service that we are designing to support it. We are starting to build more accurate risk profiles of the farmers we work with and we think that is pretty damn exciting.

An insight that consistently surfaces when working with farmers in emerging markets is the need for financing: at the end of a season to buy new inputs, during the year to pay school fees, bridging after a poor harvest, to cover the capital expense of moving towards increased production, and on, and on. There is nothing surprising about it. Commercial capital is a vehicle to growth and stability in every economy. In Myanmar, that is no different. While our partner, Proximity Designs, is leading the charge to fill a massive capital deficit across the country, we wanted to see what was working elsewhere and how others were approaching the design of financial products for small farmers.

In Kenya, we spent time with Juhudi Kilimo, which has built a strong loan book through the provision of asset financing and micro insurance to smallholder, traditionally high-risk farmers and Acre, which has been using heavy data analysis build an insurance business across a similar demographic. What was inspiring to the IDEO.org team generally was the sophistication of thinking around risk profiling and the number of techniques being applied or thought about by these companies. We have listed a few of those techniques below:

PSYCHOMETRIC TESTING

We discussed psychometric analysis and in particular the techniques used by EFL Global to build intuition around someone’s personality more deeply and translate their individual traits into a projected credit worthiness score.

MACRO DATA ANALYSIS

With Acre, the conversation centered around their networks of weather stations, area yields, and weather index data, all of which is used to build projections for what a farmers yield should look like and set lower bounds to trigger insurance payouts.

PROXY MEASURES

We also spoke about the possibility of using proxy measures for financial reliability like cell phone usage patterns, which can describe the availability of small amounts of capital and the likelihood that a person might currently have the means to repay a basic loan.

GROUP LOANS

We spent most of our time speaking about the most common method used across the MFI world for risk mitigation, group loans. The thinking here, and it is correct, is that a self-selected group can manage risk by only selecting credit worthy members.

THE MISSING DATA POINT

The variable though that is the hardest to control for and the one which has the single largest impact on the risk profile of a farmer is the decision making of that farmer himself.

We know that there are a series of data for individual crops that describe what an optimal output could look like. Generally speaking, a farmer is looking to navigate those variables that impact their ability to achieve that optimal output. How we react to weather events, how much we irrigate and when, what type of fertilizer we apply, at what time, and how, then, all matter. Too much water applied at the wrong time? Expect suboptimal plant health, increased spend on fertilizer, reduced overall income for the farmer and, crucially for what we are talking about here, reduced ability to repay a loan and increased likelihood that there will have to be an insurance payout.

In short, suboptimal decisions make business more difficult for financial service providers. We are building a sensor-based product that will allow farmers to irrigate more precisely based on real time soil data. We will build that into a decision support tool, which allows for more accurate application of fertilizer and pesticides. This product then, and the data it captures, is really acting as a decision monitor, showing us the farmers who reacted to an event optimally and those who reacted suboptimally. Armed with this data, very many things are possible, one of which is better targeting of loan products and the more accurate pricing of insurance and another of which could be a baseline for real contract farming and finally, a futures market for smallholder production. Big goals, sure, but this is not a small challenge.